Cross border transaction machine translation

ABSTRACT

A user query for items is received in a first language and translated from the first language to a second language. A result set in the second language that meets the query is obtained and is translated into the first language for presentation to the user. User feedback is used to build an ontology for optimizing the translation from the first language to the second language based on query context and the feedback. Query context may include information determined by learning semantic relationships between keywords in the query. Optimizing may include building an ontology used by a machine translator to translate key words from the first language to the second language. The number of items in the result set are measured or information is abstracted from the feedback and correlated to ontological information of the result set. The system adapts to changes in meanings in the first language over time.

CLAIM OF PRIORITY

This application claims the benefit of priority to U.S. ProvisionalPatent Application Ser. No. 61/946,658, filed on Feb. 28, 2014, which isincorporated by reference herein in its entirety.

TECHNICAL FIELD

The present application relates generally to electronic commerce and, inone specific example, to techniques for machine translation forecommerce transactions.

BACKGROUND

The use of mobile devices, such as cellphones, smartphones, tablets, andlaptop computers, has increased rapidly in recent years, which, alongwith the rise in dominance of the Internet as the primary mechanism forcommunication, has caused an explosion in electronic commerce(“ecommerce”). As these factors spread throughout the world,communications between users that utilize different spoken or writtenlanguages increase exponentially. Ecommerce has unique challenges whendealing with differing languages being used, specifically an ecommercetransaction often involves the need to ensure specific information isaccurate. For example, if a potential buyer asks a seller about someaspect of a product for sale, the answer should be precise and accurate.Any failing in the accuracy of the answer could result in a lost sale oran unhappy purchaser.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which:

FIG. 1 is a network diagram depicting a client-server system, withinwhich one example embodiment may be deployed.

FIG. 2 is a block diagram illustrating marketplace and paymentapplications and that, in one example embodiment, are provided as partof application server(s) in the networked system.

FIG. 2A is a block diagram illustrating an example machine translationapplication according to an example embodiment.

FIG. 3 is a block diagram illustrating a method of optimizing machinetranslation so that it is focused on ecommerce search terms, words orphrases language translation according to an example embodiment.

FIG. 4 is a flowchart illustrating an example method, consistent withvarious embodiments.

FIG. 5 is a block diagram illustrating a mobile device, according to anexample embodiment.

FIG. 6 is a block diagram of a machine in the example form of a computersystem within which instructions may be executed for causing the machineto perform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

Example methods and systems for machine translation (MT) for ecommerceare provided. It will be evident, however, to one of ordinary skill inthe art that the present inventive subject matter may be practicedwithout these specific details.

According to various exemplary embodiments, MT for ecommerce comprisestranslating a query in a first language to a query in a second languageand querying an ecommerce database that is maintained in the secondlanguage to obtain a result set of items in the second language thatmeet the query that is in the first language. Relevancy of the resultset of items to the user of the first language is measured and is usedto form an ontology that may be used to optimize translation of queriesin the first language to queries in the second language. In exampleembodiments the languages of Russian and English are used as the firstlanguage and the second language, respectively, but it will be evidentto one of ordinary skill in the art that any two languages may be usedas the first and second languages.

FIG. 1 is a network diagram depicting a client-server system 100, withinwhich one example embodiment may be deployed. A networked system 102, inthe example forms of a network-based marketplace or publication system,provides server-side functionality, via a network 104 (e.g., theInternet or a Wide Area Network (WAN)), to one or more clients. FIG. 1illustrates, for example, a web client 106 (e.g., a browser, such as theInternet Explorer browser developed by Microsoft Corporation of Redmond,Wash. State) and a programmatic client 108 executing on respectivedevices 110 and 112.

An Application Program Interface (API) server 114 and a web server 116are coupled to, and provide programmatic and web interfaces respectivelyto, one or more application servers 118. The application servers 118host one or more marketplace applications 120 and payment applications122. The application servers 118 are, in turn, shown to be coupled toone or more database servers 124 that facilitate access to one or moredatabases 126.

The marketplace applications 120 may provide a number of marketplacefunctions and services to users who access the networked system 102. Thepayment applications 122 may likewise provide a number of paymentservices and functions to users. The payment applications 122 may allowusers to accumulate value (e.g., in a commercial currency, such as theU.S. dollar, or a proprietary currency, such as “points”) in accounts,and then later to redeem the accumulated value for products (e.g., goodsor services) that are made available via the marketplace applications120. While the marketplace and payment applications 120 and 122 areshown in FIG. 1 to both form part of the networked system 102, it willbe appreciated that, in alternative embodiments, the paymentapplications 122 may form part of a payment service that is separate anddistinct from the networked system 102.

Further, while the system 100 shown in FIG. 1 employs a client-serverarchitecture, the embodiments are, of course, not limited to such anarchitecture, and could equally well find application in a distributed,or peer-to-peer, architecture system, for example. The variousmarketplace and payment applications 120 and 122 could also beimplemented as standalone software programs, which do not necessarilyhave networking capabilities.

The web client 106 accesses the various marketplace and paymentapplications 120 and 122 via the web interface supported by the webserver 116. Similarly, the programmatic client 108 accesses the variousservices and functions provided by the marketplace and paymentapplications 120 and 122 via the programmatic interface provided by theAPI server 114. The programmatic client 108 may, for example, be aseller application (e.g., the TurboLister application developed by eBayInc., of San Jose, Calif.) to enable sellers to author and managelistings on the networked system 102 in an off-line manner, and toperform batch-mode communications between the programmatic client 108and the networked system 102.

FIG. 1 also illustrates a third party application 128, executing on athird party server machine 130, as having programmatic access to thenetworked system 102 via the programmatic interface provided by the APIserver 114. For example, the third party application 128 may, utilizinginformation retrieved from the networked system 102, support one or morefeatures or functions on a website hosted by the third party. The thirdparty website may, for example, provide one or more promotional,marketplace, or payment functions that are supported by the relevantapplications of the networked system 102.

FIG. 2 is a block diagram illustrating marketplace and paymentapplications 120 and 122 that, in one example embodiment, are providedas part of application server(s) 118 in the networked system 102. Asused herein, applications may be referred to as modules. Theapplications 120 and 122 may be hosted on dedicated or shared servermachines (not shown) that are communicatively coupled to enablecommunications between server machines. The applications 120 and 122themselves are communicatively coupled (e.g., via appropriateinterfaces) to each other and to various data sources, so as to allowinformation to be passed between the applications 120 and 122 or so asto allow the applications 120 and 122 to share and access common data.The applications 120 and 122 may furthermore access one or moredatabases 126 via the database servers 124.

The networked system 102 may provide a number of publishing, listing,and price-setting mechanisms whereby a seller may list (or publishinformation concerning) goods or services for sale, a buyer can expressinterest in or indicate a desire to purchase such goods or services, anda price can be set for a transaction pertaining to the goods orservices. To this end, the marketplace and payment applications 120 and122 are shown to include at least one publication application 200 andone or more auction applications 202, which support auction-formatlisting and price setting mechanisms (e.g., English, Dutch, Vickrey,Chinese, Double, Reverse auctions, etc.). The various auctionapplications 202 may also provide a number of features in support ofsuch auction-format listings, such as a reserve price feature whereby aseller may specify a reserve price in connection with a listing and aproxy-bidding feature whereby a bidder may invoke automated proxybidding.

A number of fixed-price applications 204 support fixed-price listingformats (e.g., the traditional classified advertisement-type listing ora catalogue listing) and buyout-type listings. Specifically, buyout-typelistings (e.g., including the Buy-It-Now (BIN) technology developed byeBay Inc., of San Jose, Calif.) may be offered in conjunction withauction-format listings, and allow a buyer to purchase goods orservices, which are also being offered for sale via an auction, for afixed-price that is typically higher than the starting price of theauction.

Store applications 206 allow a seller to group listings within a“virtual” store, which may be branded and otherwise personalized by andfor the seller. Such a virtual store may also offer promotions,incentives, and features that are specific and personalized to arelevant seller.

Reputation applications 208 allow users who transact, utilizing thenetworked system 102, to establish, build, and maintain reputations,which may be made available and published to potential trading partners.Consider that where, for example, the networked system 102 supportsperson-to-person trading, users may otherwise have no history or otherreference information whereby the trustworthiness and credibility ofpotential trading partners may be assessed. The reputation applications208 allow a user (for example, through feedback provided by othertransaction partners) to establish a reputation within the networkedsystem 102 over time. Other potential trading partners may thenreference such a reputation for the purposes of assessing credibilityand trustworthiness.

Personalization applications 210 allow users of the networked system 102to personalize various aspects of their interactions with the networkedsystem 102. For example a user may, utilizing an appropriatepersonalization application 210, create a personalized reference page atwhich information regarding transactions to which the user is (or hasbeen) a party may be viewed. Further, a personalization application 210may enable a user to personalize listings and other aspects of theirinteractions with the networked system 102 and other parties.

The networked system 102 may support a number of marketplaces that arecustomized, for example, for specific geographic regions. A version ofthe networked system 102 may be customized for the United Kingdom,whereas another version of the networked system 102 may be customizedfor the United States. Each of these versions may operate as anindependent marketplace or may be customized (or internationalized)presentations of a common underlying marketplace. The networked system102 may accordingly include a number of internationalizationapplications 212 that customize information (and/or the presentation ofinformation by the networked system 102) according to predeterminedcriteria (e.g., geographic, demographic or marketplace criteria). Forexample, the internationalization applications 212 may be used tosupport the customization of information for a number of regionalwebsites that are operated by the networked system 102 and that areaccessible via respective web servers 116.

Navigation of the networked system 102 may be facilitated by one or morenavigation applications 214. For example, a search application (as anexample of a navigation application 214) may enable key word searches oflistings published via the networked system 102. A browse applicationmay allow users to browse various category, catalogue, or inventory datastructures according to which listings may be classified within thenetworked system 102. Various other navigation applications 214 may beprovided to supplement the search and browsing applications.

In order to make listings available via the networked system 102 asvisually informing and attractive as possible, the applications 120 and122 may include one or more imaging applications 216, which users mayutilize to upload images for inclusion within listings. An imagingapplication 216 also operates to incorporate images within viewedlistings. The imaging applications 216 may also support one or morepromotional features, such as image galleries that are presented topotential buyers. For example, sellers may pay an additional fee to havean image included within a gallery of images for promoted items.

Listing creation applications 218 allow sellers to conveniently authorlistings pertaining to goods or services that they wish to transact viathe networked system 102, and listing management applications 220 allowsellers to manage such listings. Specifically, where a particular sellerhas authored and/or published a large number of listings, the managementof such listings may present a challenge. The listing managementapplications 220 provide a number of features (e.g., auto-relisting,inventory level monitors, etc.) to assist the seller in managing suchlistings. One or more post-listing management applications 222 alsoassist sellers with a number of activities that typically occurpost-listing. For example, upon completion of an auction facilitated byone or more auction applications 202, a seller may wish to leavefeedback regarding a particular buyer. To this end, a post-listingmanagement application 222 may provide an interface to one or morereputation applications 208, so as to allow the seller conveniently toprovide feedback regarding multiple buyers to the reputationapplications 208.

Dispute resolution applications 224 provide mechanisms whereby disputesarising between transacting parties may be resolved. For example, thedispute resolution applications 224 may provide guided procedureswhereby the parties are guided through a number of steps in an attemptto settle a dispute. In the event that the dispute cannot be settled viathe guided procedures, the dispute may be escalated to a third partymediator or arbitrator.

A number of fraud prevention applications 226 implement fraud detectionand prevention mechanisms to reduce the occurrence of fraud within thenetworked system 102.

Messaging applications 228 are responsible for the generation anddelivery of messages to users of the networked system 102 (such as, forexample, messages advising users regarding the status of listings at thenetworked system 102 (e.g., providing “outbid” notices to bidders duringan auction process or to provide promotional and merchandisinginformation to users)). Respective messaging applications 228 mayutilize any one of a number of message delivery networks and platformsto deliver messages to users. For example, messaging applications 228may deliver electronic mail (e-mail), instant message (IM), ShortMessage Service (SMS), text, facsimile, or voice (e.g., Voice over IP(VoIP)) messages via the wired (e.g., the Internet), plain old telephoneservice (POTS), or wireless (e.g., mobile, cellular, WiFi, WiMAX)networks 104.

Merchandising applications 230 support various merchandising functionsthat are made available to sellers to enable sellers to increase salesvia the networked system 102. The merchandising applications 230 alsooperate the various merchandising features that may be invoked bysellers, and may monitor and track the success of merchandisingstrategies employed by sellers.

The networked system 102 itself, or one or more parties that transactvia the networked system 102, may operate loyalty programs that aresupported by one or more loyalty/promotions applications 232. Forexample, a buyer may earn loyalty or promotion points for eachtransaction established and/or concluded with a particular seller, andbe offered a reward for which accumulated loyalty points can beredeemed.

A machine translation application 234 may translate a query in a firstlanguage to a query in a second language, obtain and build an ontologybased on terms (words and/or phrases) of the query in the first languagethat may be combined with automatically extracted additional informationfrom an ontology and user feedback indicating the relevancy of a resultset obtained by the query that is translated into the second language.This ontology is defined by the user set of the first language and maybe enriched by measuring the relevancy of the result set. A moredetailed view of a machine translation application in accordance with anembodiment is seen in FIG. 2A.

Machine translation application 234 is seen in additional detail in FIG.2A. FIG. 2A is a block diagram illustrating an example machinetranslation application according to an example embodiment. The machinetranslation application comprises Russian to English translation module236, English to Russian translation module 238, and ontology buildapplication 240. Russian to English translation module 236 may be usedto translate a query in the Russian language to a query in the Englishlanguage, as more fully described at 306 in FIG. 3. English to Russiantranslation module 238 may be used to translate an English result setfrom the English language to the Russian language, as more fullydescribed at 318 in FIG. 3. Ontology build application 240 may be usedto build an ontology that may be used to optimize translating a queryfrom the Russian language to a query in the English language, also asmore fully discussed with respect to FIG. 3. Ontology build application240 comprises user feedback monitoring module 242, query contextlearning module 244 and translation optimizing module 246. User feedbackmonitoring module 242 may be used to monitor user feedback for measuringrelevancy of a result set that is provided to a user in response to aquery in the Russian language, as more fully described at 318 of FIG. 3.Query context learning module 244 may be used to learn semanticrelationships between keywords in a query as more fully described at 328of FIG. 3. Translation optimizing module 246 may be used to optimize thetranslation of the Russian query to the English query as more fullydescribed with respect to 306 of FIG. 3. The operation of the abovemodules comprising machine translation application 234 is also morefully described with respect to the method illustrated in the flowchartof FIG. 4.

Machine translation (MT) is usually focused on the translation ofregular sentences of text, from political text, technical descriptions,and the like. However, MT has heretofore not been focused on the needsof a user of an ecommerce system (or other publication system). In MT ofregular sentences, the objective is usually to maximize fluency, such aspieces of fluently readable text. In MT for ecommerce, however, theobjective is not maximizing pieces of fluent, readable text but ratherfidelity of translation of the translation units or terms. In otherwords, when a user enters a query in an ecommerce system, the systemmight focus on the translation of the keywords so that the items thesystem returns to the user are items that the user considers to havehighest possible semantic value, or relevancy, to the query. A query mayinclude one or more keywords describing the product or service that theuser is searching for. Standard MT from one language to another, forexample, Russian to English, is directed to surface forms of the text.In this standard type of missing a word or interchanging the may nothave a seriously negative impact on the reader (for example using “blue”as a simplification of “navy blue”, or translating “crimson” into“red”). However, in ecommerce, losing or altering even one semanticcomponent of a user query (or keyword description of the item queried)might result in the user not purchasing the queried item from theecommerce system or, worse, purchasing the wrong item, and in eithercase experiencing the user session as a negative experience. This couldtend to motivate the user not to use that particular ecommerce system inthe future, which is a loss to the ecommerce system. Consequently, in MTfor ecommerce the focus can be said to be primarily on fidelity, notfluency, where fidelity can be viewed as returning to the user a list ofitems that have semantic relevancy to the user query.

Stated another way, using metrics that measure how readable translatedtext is, such as is done using the BLEU (Bilingual EvaluationUnderstudy) or METEOR (Metric for Evaluation of Translation withExplicit Ordering) metrics, may result in fluent translations. But, asalluded to above, in ecommerce the objective is to obtain precision andrecall such that the overall machine translation provides a goodexperience for the user, by providing the user quick access to thequeried item with little or no error. Otherwise the user may make a haddecision because incorrect items were returned to the user, the resultbeing an unhappy customer who is unlikely to be a repeat customer.Hence, BLEU or METEOR metrics are of little use in MT for ecommerce.

Therefore a new metric is needed determine whether the item set that isreturned to the user in response to the query results in a good userexperience. This metric would measure user feedback related to thereturned item set. The probability of providing a good user experiencemay be increased by combining MT with user feedback.

Products and services offered for sale on ecommerce web sites are listedin multiple languages on multiple web sites, for example, ecommercesystems such as eBay maintain web sites in different languages fordifferent countries. eBay has sites in the United States, the UK,Russia, Spain, France, and others. FIG. 3 is a block diagramillustrating a method of optimizing machine translation so that it isfocused on ecommerce keywords language translation according to anexample embodiment. FIG. 3 describes a method in which a query in theRussian language may be translated into English using MT, and theEnglish translation of the Russian query would be used to search anecommerce system's English database for the item that was queried inRussian. User feedback relating to returned items, and word context,allows an ontology to be built that may be used to optimize theforegoing translation.

FIG. 3 illustrates three layers, a user layer, an MT layer, andecommerce layer. A user may enter a query for an item at query interface302 in the Russian language. The query is coupled over 304 to a Russianto English translation application 306 in the MT layer. The output 308is the English translation of the Russian query and may be coupled to anecommerce query application 310 in the ecommerce layer. The site atwhich the English translation query 308 is used may be an Englishlanguage site, such as eBay's United States site or UK site. A database314 is queried to access items in the ecommerce site inventory that meetthe query. The result of that query, called here the English result setand which may be ecommerce listings for items that meet the query, istransmitted over 316 to English to Russian translation application 318in the MT layer. The listings of items in the English result set 316 maybe translated back into Russian by English to Russian translationapplication 318 to obtain a user list 322 of items in Russian, which istransmitted to the Russian user.

In typical MT having to do with text, there would be an interest incomparing the Russian query at 304 to the English translation of thatRussian query at 308, per se. However, as discussed, the objective in MTfor ecommerce is quite different. In MT for ecommerce the concern is notthe comparison of the Russian to English translation. Instead, theconcern is fidelity, or how relevant the English result set 316 is tothe Russian query. In other words, the actual translation may even beviewed as hidden because what is important in MT for ecommerce is thefidelity of the result sets presented to the Russian user, responsive tothe Russian query.

Measuring the fidelity, or relevancy, of the return set may be doneusing ecommerce metrics of recall and precision. Recall, as used in thiscontext, is a measure of how large is the number of listings in therecall set. Precision is a measure of how much of that recall set isrelevant to the query. High recall and high precision is the result thatis sought after, implying that the result set is large (high recall) andcomprises primarily relevant item listings (high precision). In oneembodiment, the number of items in the English result set 316 may beused as a measure of recall. The higher the number of items in theresult set the better the translation, usually, because if thetranslation were poor there would be relatively few items returned inthe English result set. In addition, the actions of the user at 302 inresponse to receiving the result set may be viewed as a positive ornegative user action. A positive user action, such as purchasing an itemin the result set, or placing a product watch after receiving the resultset, may be monitored by ontology build application 328, and used tobuild an ontology over a massive number of user sessions. The ontologymay then be used at 330 to optimize the Russian to English translationat Russian to English translation application 306.

In one embodiment, feedback provides an indication of the relevancy ofthe result sets 324. The relevancy may be measured by the ontology buildapplication 328 that builds an ontology by abstracting information fromthe user feedback at 326 and correlating these at 332 to the ontologicalinformation of the derived user list 322. This measurement of therelevancy of results set 324 may be accomplished in a number of ways.One way of measuring relevancy of the result set 324 is by explicit userfeedback like a star rating from the user where one star may indicatethat the result set 324 was not very relevant and five stars mayindicate that the result set 324 was highly relevant. Another way ofmeasuring relevancy of the result set 324 is by implicit user feedback.This may be accomplished by observing actions the user takes afterreceiving the result set. For example, if the user buys a product fromthe result set 324, or sets a watch for a product from the result set324, this may be viewed as an implicit user feedback indication that theresult set was relevant.

With continued reference to FIG. 3, the ontology of relationships can bebuilt as alluded to above by ontology build application 328 from thedata of user sessions by focusing on keywords in the Russian query. Thismay be done continuously by ontology build application 328. Largeecommerce systems such as eBay host millions user sessions each day. Bylooking at a massive number of user sessions continuously, the ontologybuild application can abstract information from user feedback and learnfrom that user feedback in order to build an ontology that can be usedto optimize MT for ecommerce. But it is the users who actually definethe ontology. Users do this by the products and services they place inthe user queries, so that ontologies do not need to be definedexplicitly by linguists but instead are learned implicitly from thedata. Also, extrapolation may be done on a continual basis with onlyslight delay due to system infrastructure and, as the ecommerce systeminfrastructure improves, the delay will become shorter and shorter sothat eventually the extrapolation from user sessions may be done innearly real time. As this process continues over years and, perhaps,over decades, changes in the meaning of words that occur over time in agiven language will be accounted for by the extrapolation from the usersessions over time. In other words, as word meanings changes, the systemwill adapt to changes in the language over time and the ontology that iscontinually being built by extrapolation will automatically account for,or reflect, those changes in meanings. Use cases may change over time,and there may be new use cases, but the ontology will be up to date withlanguage changes over time. If the ontology were defined explicitly bylinguists, the system would not adapt to changes in language meaningover time.

As one extrapolation example, consider a result set 324 that is relevantbecause it results in a positive action by the user, such as a purchase,or the like. If the user query that resulted in the positive actiondescribes “dress” and “burgundy,” then the system learns that “burgundy”in the context of “dress” cannot the same as “burgundy” in the contextof “glass” (like a burgundy wine glass). Assume for the sake of example,that statistically ten users query “glass” in Russian and receive resultsets from the English ecommerce site. If four of those users describethe query to include “burgundy” and “glass,” and also describe in thequery “red” and “wine,” the context of red, wine, glass, and burgundyoccur together (and provide positive feedback as to relevancy of theresult set), the system will extrapolate and learn from these words incontext and can connect the semantic concepts together. As such, theprobability that the queries relate to “wine glass,” or “red wine glass”may be very high. As another example, if a Russian user queries thatresulted in positive user feedback describes “dress” and “burgundy,” theprobability is higher that the query is about a burgundy colored dressthan that the query relates to wine, or to the Burgundy region ofFrance. There may be other clues in the query, such as describing “red”along with “burgundy” and “dress” so that the system will learn, that,in Russian, the query is more likely to be about a red dress than aboutred wine or about the Burgundy region of France. Here the system willlearn that “red” and “burgundy” used in the context of “dress” impliesthat red and burgundy are similar colors. This learning may be fromextrapolating from a significant amount of data, such as user sessions,given that large ecommerce sites like eBay host millions of usersessions in one day. For smaller ecommerce sites, the system may have towait until a statistically significant amount of data can be gatheredfrom user sessions.

FIG. 4 is a flowchart illustrating an example method, consistent withvarious embodiments. At 410 of FIG. 4 a user enters a query in a firstlanguage, here Russian, as at 302 of FIG. 3. Russian to Englishtranslation module 236 of FIG. 2A then translates the query into asecond language, here English, at 420 of FIG. 4. The ecommerce systemthen queries an ecommerce database at 430, the database being maintainedin the English language. The ecommerce system obtains result sets fromthe database query as at 440 of FIG. 4. This result set is obtained fromdatabase 314 of FIG. 3 and at 450 of FIG. 5 the MT system translates theresult set into the first language, here Russian. This translation isperformed by English to Russian translation module 238 of FIG. 2A. At460 of FIG. 4 the system sends the results set to the user in the firstlanguage, here Russian. This is seen at 324 in FIG. 3. At 470 of FIG. 4the user provides feedback based on the result set, as discussed withrespect to transmission of implicit user feedback over line 326 of FIG.3. At 480 of FIG. 4 the system monitors the user feedback and builds anontology based on a query context and the user feedback as explainedwith respect to ontology build application 328 of FIG. 3. Thismonitoring is undertaken by user feedback monitoring module 242 of theontology build application 240 of FIG. 2A. Positive user feedback may beevaluated by the query context learning module 244 that then learnssemantic relationships between keywords in the query as more fullydescribed at 328 of FIG. 3. Using the user feedback and the querycontexts, the ontology build application builds the ontology and thetranslation optimizing module 246 optimizes the process of translationfrom the first language, here Russian, to the second language, hereEnglish, using the ontology as more fully discussed with respect toontology build 328 of FIG. 3.

Example Mobile Device

FIG. 5 is a block diagram illustrating a mobile device 500, according toan example embodiment. The mobile device 500 may include a processor502. The processor 502 may be any of a variety of different types ofcommercially available processors suitable for mobile devices (forexample, an XScale architecture microprocessor, a microprocessor withoutinterlocked pipeline stages (HIPS) architecture processor, or anothertype of processor 502). A memory 504, such as a random access memory(RAM), a flash memory, or other type of memory, is typically accessibleto the processor 502. The memory 504 may be adapted to store anoperating system (OS) 506, as well as application programs 508, such asa mobile location enabled application that may provide LBSs to a user.The processor 502 may be coupled, either directly or via appropriateintermediary hardware, to a display 510 and to one or more input/output(I/O) devices 512, such as a keypad, a touch panel sensor, a microphone,and the like. Similarly, in some embodiments, the processor 502 may becoupled to a transceiver 514 that interfaces with an antenna 516. Thetransceiver 514 may be configured to both transmit and receive cellularnetwork signals, wireless data signals, or other types of signals viathe antenna 516, depending on the nature of the mobile device 500.Further, in some configurations, a GPS receiver 518 may also make use ofthe antenna 516 to receive GPS signals.

Modules, Components and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied (1) on machine-readable storage or(2) in a transmission signal) or hardware-implemented modules. Ahardware-implemented module is a tangible unit capable of performingcertain operations and may be configured or arranged in a certainmanner. In example embodiments, one or more computer systems (e.g., astandalone, client or server computer system) or one or more processors502 may be configured by software (e.g., an application or applicationportion) as a hardware-implemented module that operates to performcertain operations as described herein.

In various embodiments, a hardware-implemented module may be implementedmechanically or electronically. For example, a hardware-implementedmodule may comprise dedicated circuitry or logic that is permanentlyconfigured (e.g., as a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an application-specific integratedcircuit (ASIC)) to perform certain operations. A hardware-implementedmodule may also comprise programmable logic or circuitry (e.g., asencompassed within a general-purpose processor or other programmableprocessor) that is temporarily configured by software to perform certainoperations. It will be appreciated that the decision to implement ahardware-implemented module mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the term “hardware-implemented module” should be understoodto encompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired) or temporarily ortransitorily configured (e.g., programmed) to operate in a certainmanner and/or to perform certain operations described herein.Considering embodiments in which hardware-implemented modules aretemporarily configured (e.g., programmed), each of thehardware-implemented modules need not be configured or instantiated atany one instance in time. For example, where the hardware-implementedmodules comprise a general-purpose processor configured using software,the general-purpose processor may be configured as respective differenthardware-implemented modules at different times. Software mayaccordingly configure processor 502, for example, to constitute aparticular hardware-implemented module at one instance of time and toconstitute a different hardware-implemented module at a differentinstance of time.

Hardware-implemented modules can provide information to, and receiveinformation from, other hardware-implemented modules. Accordingly, thedescribed hardware-implemented modules may be regarded as beingcommunicatively coupled. Where multiple of such hardware-implementedmodules exist contemporaneously, communications may be achieved throughsignal transmission (e.g., over appropriate circuits and buses thatconnect the hardware-implemented modules). In embodiments in whichmultiple hardware-implemented modules are configured or instantiated atdifferent times, communications between such hardware-implementedmodules may be achieved, for example, through the storage and retrievalof information in memory structures to which the multiplehardware-implemented modules have access. For example, onehardware-implemented module may perform an operation, and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware-implemented module may then,at a later time, access the memory device to retrieve and process thestored output. Hardware-implemented modules may also initiatecommunications with input or output devices, and can operate on aresource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors 502 that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors 502 may constitute processor-implementedmodules that operate to perform one or more operations or functions. Themodules referred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods described herein may be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod may be performed by one or more processors 502 orprocessor-implemented modules. The performance of certain of theoperations may be distributed among the one or more processors 502, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor 502 or processors502 may be located in a single location (e.g., within a homeenvironment, an office environment or as a server farm), while in otherembodiments the processors 502 may be distributed across a number oflocations.

The one or more processors 502 may also operate to support performanceof the relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., application program interfaces (APIs).)

Electronic Apparatus and System

Example embodiments may be implemented in digital electronic circuitry,or in computer hardware, firmware, software, or in combinations of them.Example embodiments may be implemented using a computer program product,e.g., a computer program tangibly embodied in an information carrier,e.g., in a machine-readable medium for execution by, or to control theoperation of, data processing apparatus, e.g., a programmable processor502, a computer, or multiple computers.

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a module, subroutine,or other unit suitable for use in a computing environment. A computerprogram can be deployed to be executed on one computer or on multiplecomputers at one site or distributed across multiple sites andinterconnected by a communication network.

In example embodiments, operations may be performed by one or moreprogrammable processors 502 executing a computer program to performfunctions by operating on input data and generating output. Methodoperations can also be performed by, and apparatus of exampleembodiments may be implemented as, special purpose logic circuitry,e.g., a field programmable gate array (FPGA) or an application-specificintegrated circuit (ASIC).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that that both hardware and software architectures meritconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor 502), or acombination of permanently and temporarily configured hardware may be adesign choice. Below are set out hardware (e.g., machine) and softwarearchitectures that may be deployed, in various example embodiments.

Example Machine Architecture and Machine-Readable Medium

FIG. 6 is a block diagram of machine in the example form of a computersystem 600 within which instructions 624 may be executed for causing themachine to perform any one or more of the methodologies discussedherein. In alternative embodiments, the machine operates as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine may operate in the capacity of aserver or a client machine in server-client network environment, or as apeer machine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a tablet PC, a set-top box(STB), a personal digital assistant (PDA), a cellular telephone, a webappliance, a network router, switch or bridge, or any machine capable ofexecuting instructions (sequential or otherwise) that specify actions tobe taken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The example computer system 600 includes a processor 602 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU) orboth), a main memory 604 and a static memory 606, which communicate witheach other via a bus 608. The computer system 600 may further include avideo display unit 610 (e.g., a liquid crystal display (LCD) or acathode ray tube (CRT)). The computer system 600 also includes analphanumeric input device 612 (e.g., a keyboard or a touch-sensitivedisplay screen), a user interface (UI) navigation (e.g., cursor control)device 614 (e.g., a mouse), a disk drive unit 616, a signal generationdevice 618 (e.g., a speaker) and a network interface device 620.

Machine-Readable Medium

The disk drive unit 616 includes a computer-readable medium 622, whichmay be hardware storage, on which is stored one or more sets of datastructures and instructions 624 (e.g., software) embodying or utilizedby any one or more of the methodologies or functions described herein.The instructions 624 may also reside, completely or at least partially,within the main memory 604 and/or within the processor 602 duringexecution thereof by the computer system 600, the main memory 604 andthe processor 602 also constituting computer-readable media 622.

While the computer-readable medium 622 is shown in an example embodimentto be a single medium, the term “computer-readable medium” may include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore instructions 624 or data structures. The term “computer-readablemedium” shall also be taken to include any tangible medium that iscapable of storing, encoding or carrying instructions 624 for executionby the machine and that cause the machine to perform any one or more ofthe methodologies of the present disclosure or that is capable ofstoring, encoding or carrying data structures utilized by or associatedwith such instructions 624. The term “computer-readable medium” shallaccordingly be taken to include, but not be limited to, solid-statememories, and optical and magnetic media. Specific examples ofcomputer-readable media 622 include non-volatile memory, including byway of example semiconductor memory devices, e.g., erasable programmableread-only memory (EPROM), electrically erasable programmable read-onlymemory (EEPROM), and flash memory devices; magnetic disks such asinternal hard disks and removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks.

Transmission Medium

The instructions 624 may further be transmitted or received over acommunications network 626 using a transmission medium. The instructions624 may be transmitted using the network interface device 620 and anyone of a number of well-known transfer protocols (e.g., HTTP). Examplesof communication networks include a local area network (“LAN”), a widearea network (“WAN”), the Internet, mobile telephone networks, plain oldtelephone (POTS) networks, and wireless data networks (e.g., WiFi andWiMax networks). The term “transmission medium” shall be taken toinclude any intangible medium that is capable of storing, encoding orcarrying instructions 724 for execution by the machine, and includesdigital or analog communications signals or other intangible media tofacilitate communication of such software. The system 600 may functionwith the Internet Protocol (IP) as a communications protocol in theInternet protocol suite for relaying datagrams across networkboundaries. The routing function of the IP enables internetworking viathe Internet. The Internet protocol suite, has the task of deliveringpackets from the source host to the destination host based on the IPaddresses in the packet headers. For this purpose, IP defines packetstructures that encapsulate the data to be delivered. It also definesaddressing methods that are used to label the datagram with source anddestination information. The connection oriented Transmission ControlProtocol (TCP) may be used, often referred to as TCP/IP. The machine mayoperate with various versions of IP, including without limitation,Internet Protocol Version 4 (IPv4), Internet Protocol Version 6 (IPv6),and may be adapted for other and future protocols. The apparatus mayfunction with various layers including an application layer, transportlayer, Internet layer and link layer. Various transport layers may beused in addition to TCP. These transport layers may include UserDatagram Protocol (UDP), Datagram Congestion Protocol (DCCP), StreamControl Transmission Protocol (SCTP), Resource Reservation Protocol(RSVP), and others.

Although the inventive subject matter has been described with referenceto specific example embodiments, it will be evident that variousmodifications and changes may be made to these embodiments withoutdeparting from the broader spirit and scope of the disclosure.Accordingly, the specification and drawings are to be regarded in anillustrative rather than a restrictive sense. The accompanying drawingsthat form a part hereof, show by way of illustration, and not oflimitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be utilized and derivedtherefrom, such that structural and logical substitutions and changesmay be made without departing from the scope of this disclosure. ThisDetailed Description, therefore, is not to be taken in a limiting sense,and the scope of various embodiments is defined only by the appendedclaims, along with the full range of equivalents to which such claimsare entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

What is claimed is:
 1. A computer implemented method comprising: receiving a query from a client machine for items, in which the query is in a first language; translating the query into a second language, including optimizing, for ecommerce, a translation of the query from the first language into the second language based at least in part on an ontology; obtaining a result set of items in the second language that meet the query; translating the result set into the first language for presentation to the client machine; and monitoring feedback from the client machine, including observing actions a user of the client machine takes relative to the result set, the actions including the user purchasing a product from the result set and placing a product watch from the result set; and building the ontology based at least on part of the observed actions such that the ontology reflects language changes over time.
 2. The method of claim 1, the method further comprising building the ontology based on query context and explicit user feedback, including a rating submitted by the user of the client machine, the rating associated with user perception of a relevance of the results of the query.
 3. The method of claim 2 wherein the query context comprises information determined by learning semantic relationships between keywords in the query.
 4. The method of claim 2, wherein building the ontology comprises at least one of measuring a number of items in the result set or abstracting information from the feedback and correlating the feedback to ontological information of the result set.
 5. The method of claim 1 wherein the query comprises a plurality of queries received over time.
 6. One or more computer-readable hardware storage device having embedded therein a set of instructions which, in response to being executed by one or more processors of a computer, causes the computer to execute operations comprising: receiving a query from a client machine for items, the query in a first language; translating the query into a second language, including optimizing, for ecommerce, a translation of the query from the first language into the second language based at least in part on an ontology; obtaining a result set of items in the second language that meet the query; translating the result set into the first language for presentation to the client machine; and monitoring feedback from the client machine, including observing actions a user of the client machine takes relative to the result set, the actions including the user purchasing a product from the result set and placing a product watch from the result set; and building the ontology based at least on part of the observed actions such that the ontology reflects language changes over time.
 7. The one or more computer readable hardware storage device of claim 6, the operations further comprising building the ontology based at least in part on query context and explicit user feedback, including a rating submitted by the user of the client machine, the rating associated with user perception of a relevance of the results of the query.
 8. The one or more computer readable hardware storage device of claim 7 wherein the query context comprises information determined from learning semantic relationships between keywords in the query.
 9. The one or more computer readable hardware storage device of claim 6, wherein building the ontology comprises at least one of measuring a number of items in the result set or abstracting information from the feedback and correlating the feedback to ontological information of the result set.
 10. The one or more computer readable hardware storage device of claim 6, wherein the query comprises a plurality of queries received over time.
 11. A non-transitory computer-readable medium having encoded therein programing code executable by one or more hardware processors to perform operations comprising: receiving a query from a client machine for items, the query in a first language; translating the query into a second language, including optimizing, for ecommerce, a translation of the query from the first language into the second language based at least in part on an ontology; obtaining a result set of items in the second language that meet the query; translating the result set into the first language for presentation to the client machine; and monitoring feedback from the client machine, including observing actions a user of the client machine takes relative to the result set, the actions including the user purchasing a product from the result set and placing a product watch from the result set; and to build the ontology based at least on part of the observed actions such that the ontology reflects language changes over time.
 12. The computer-readable medium of claim 11, the operations further comprising building the ontology based at least in part on the observed actions and further based at least in part on explicit user feedback, including a rating submitted by the user of the client machine, the rating associated with user perception of a relevance of the results of the query.
 13. The computer-readable medium of claim 12, the operations further comprising determining the query context by learning semantic relationships between keywords in the query.
 14. The computer-readable medium of claim 11, the operations further comprising measuring a number of items in the result set or abstract information from the feedback and to correlate the feedback to ontological information of the result set.
 15. The computer-readable medium of claim 11 wherein the query comprises a plurality of queries received over time. 